Hema A. Murthy
Indian Institute of Technology Madras
252 Papers
1.2K Citations
Hema A. Murthy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Computer science & Speaker recognition. The author has an hindex of 29, co-authored 230 publications. Previous affiliations of Hema A. Murthy include International Institute of Information Technology, Hyderabad & Indian Institutes of Technology.
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Papers
A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders
Ashish Mishra,M Shiva Krishna Reddy,Anurag Mittal,Hema A. Murthy +3 more
- 01 Jun 2018
TL;DR: In this paper, a conditional variational autoencoder (CVAE) is used to generate the samples from the given attributes and use the generated samples for classification of the unseen classes.
Significance of the Modified Group Delay Feature in Speech Recognition
TL;DR: The group delay function is modified to overcome the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects and is called the modified group delay feature (MODGDF).
207
The modified group delay function and its application to phoneme recognition
Hema A. Murthy,V. Gadde +1 more
- 06 Apr 2003
TL;DR: A new spectral representation of speech signals through group delay functions through cepstral coefficients is explored, which reduces the effects of zeroes close to the unit circle in the z-domain and these clutter the spectra.
168
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A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
TL;DR: In this paper, a conditional variational autoencoder (CVAE) is used to generate the samples from the given attributes and use the generated samples for classification of the unseen classes.
143
Group delay functions and its applications in speech technology
Hema A. Murthy,B. Yegnanarayana +1 more
TL;DR: The need to exploit the potential of the group delay functions for development of speech systems is demonstrated by demonstrating the effectiveness of segmentation of speech, and the features derived from the modified group delay are demonstrated in applications such as language identification, speech recognition and speaker recognition.